FI:PV069 Hybrid Systems of Machine Lear - Course Information

PV069 Hybrid Systems of Machine Learning

Extent and Intensity

2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).

Teacher(s)

Mgr. Tomáš Hudík (seminar tutor)
doc. Ing. Jan Žižka, CSc. (lecturer)

Guaranteed by

prof. RNDr. Jiří Hřebíček, CSc.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Jan Žižka, CSc.

Timetable

Wed 9:00–10:50 C511 and each even Thursday 14:00–15:50 B117

Prerequisites (in Czech)

! P069 Hybrid Systems of Machine Learning

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Applied Informatics (programme FI, B-AP)
• Applied Informatics (programme FI, N-AP)
• Informatics with another discipline (programme FI, B-BI)
• Informatics with another discipline (programme FI, B-FY)
• Informatics with another discipline (programme FI, B-GE)
• Informatics with another discipline (programme FI, B-GK)
• Informatics with another discipline (programme FI, B-CH)
• Informatics with another discipline (programme FI, B-IO)
• Informatics with another discipline (programme FI, B-MA)
• Informatics with another discipline (programme FI, B-SO)
• Informatics with another discipline (programme FI, B-TV)
• Informatics (programme FI, B-IN)
• Informatics (programme FI, M-IN)
• Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)

Course objectives

This subject introduces basic principles of artificial neural networks and their applications from the practical point of view. It focuses on utilization of combined (hybrid) machine learning methods both for applications and automated support of designing learning algorithms by selected machine learning methods.

Syllabus

• Artificial perceptrons and neural networks. Basic learning algorithms, delta-rule, back propagation of errors. Features of basic neural network models, problems with over-training and network designs.
• Transformation of decision trees to neural networks, initialization of weigths.
• Genetic algorithms, their combinations with neural networks, optimalization of weigths.
• Hybrid neural networks, combination of inputs and weights using t-norms and t-conorms. AND and OR fuzzy neuron. Fuzzy-neural networks. ANFIS and NEFCON architectures. Neuro-fuzzy classifiers. Optimalization of fuzzy sets within IF-THEN rules.
• Recurrent, Hopfield, and avalanche networks. Other types of networks.
• Application examples.

Literature

• MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info

Language of instruction

Czech

Further Comments

Study Materials

• Enrolment Statistics (Spring 2006, recent)
  
• Permalink: https://is.muni.cz/course/fi/spring2006/PV069